Smarter Digital Workers to Handle Unstructured Content

Thursday, February 7, 2019 - 11:50

By Bill Galusha, ABBYY

In today’s world, enterprise organisations increasingly rely on digital automation to deliver the greatest level of efficiency and customer experience. A catalyst to an organisations digital transformation journey has been robotic process automation (RPA), creating a new class of digital workers that replace or augmented many office employee jobs increasing efficiency, scale, and speed at which work can be done.

Despite the push towards more digitisation of processes, organisations are still faced with a daunting challenge when it comes to processes involving content, information that is not always well structured and often takes a team of employees to manage.

Virtually every industry and business department still rely heavily on documents in digital or printed format coming from all different communication channels of input – email, fax, mobile, and scanners.

These document processes put an enormous strain on operations and their employees as they are required to interpret the information and process it accordingly resulting in manually extracting relevant data from the document and putting into a system.

Intelligent automation skills

Imagine if Blue Prism’s digital workers (software robots) could recognise, read, and understand a document regardless if it were a form or an unstructured document in digital or image format? The reality is you don’t have to imagine this, it is reality.

Incorporate machine learning to perpetually improve and streamline business processes.

Measure, sustain, and adapt digitised content processes over time.

Learn and adapt to unstructured content

By providing Blue Prism digital workers with the right skills, it opens a new world to automating of content related processes. There is unstructured content in business areas like finance and accounting (invoices, purchase orders, sales orders); logistics (customs declarations, proof of delivery, bills of lading); financial services (mortgage lending, opening accounts, trade confirmation); and insurance (claims, policy administration, opening accounts).

The key to scaling the solution and using across many business areas, it needs to adapt to the different document types and variations.

By using machine learning, the system does not rely on a fixed template rather it learns by analysing the document layout and text, along with capturing user input during the process. This allows for the system to get smarter and more accurate over time as it encounters new document types and variations.

This makes it possible to automate document processing like invoices or purchase orders where there’s potentially thousands of variations of these documents coming from many regions and in different languages.

As organisations expand their use of RPA and look to the next level of intelligent automation, an effective digital worker is going to need the skills to visually look at a document, determine what is, extract the relevant data, and process it. Today, that solution exist.